The continuous innovation of technology is changing the way companies operate across different sectors. In particular, big data and artificial intelligence (AI) are expected to have the most significant impact. In this post, we’ll discuss how big data and AI can be used to support clinical trials in a variety of ways.
How does big data change clinical trials?
Big data can enable pharmaceutical companies to find the best solutions for quick and straightforward clinical trials. It can also help reduce the costs of tests and establish evidence that is more objective. With the automatic collection of unbiased and consistent sensor data and quantifiable measurement scales, pharma researchers can measure greater precision and accuracy. They can use the objective evidence to demonstrate and assess a new treatment’s clinical performance, safety, effectiveness and most importantly, its side effects.
Big data can enable the remote monitoring of data, which can provide an essential foundation for analyzing deeper insights on how drugs affect the quality of life and symptom progression. There are also technologies developed to make a comprehensive solution to collect, store and process data from the patients.
How can AI be used in clinical trials?
Learn how AI/ML can be used by pharmaceutical and medical device companies to improve the clinical data review and cleansing process.
AI can automate many processes in clinical trials, mainly in the pharmaceutical sector. The following are some of the applications AI can support:
Text Mining for Clinical Trial Planning
With this application, past trial information can be leveraged to inform the current trial planning. This data-driven method can reduce overall study cost and expenses without compromising on study reach and effectiveness.
Clinical Trial Optimization and Design
This application of AI can again take past trial design and execution data, and directly inform the study design process in a significant insightful way to ensure the greater likelihood of successful, optimized clinical trial design. In addition, real-time data access and analysis will allow companies to make modifications to trial designs in a very quick and effective way during the study. Similarly, real-time data analysis through AI can also more quickly identify issues with ineffective studies, such that those studies can be halted sooner and therefore lead to significant cost savings.
With poor and mismanaged patient recruitment being one of the biggest challenges to trials today, the use of AI in this area can significantly improve effectiveness in this area. Instead of relying on physicians to identify eligible patients for trials, AI can use patients’ electronic health records and other personal attributes, and reference available and upcoming trials to match patients to trials effectively.
While physicians attempt to do this manually, AI’s ability to process the vast amounts of data available on both patients and trials is more efficient and can have greater success rates. In addition, applications such as this, can empower patients to add additional criteria and other personal health record data to further refine the list of trials for which they are best suited. This creates a much more personalized experience and puts the patients in the control of their own data.
To learn more about using AI for clinical trials, please reach out to me.